First-Order Logic with Isomorphism
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Reasoning About Equations Using Equality
Mathematics Instructional Plan – Grade 4 Reasoning About Equations Using Equality Strand: Patterns, Functions, Algebra Topic: Using reasoning to assess the equality in closed and open arithmetic equations Primary SOL: 4.16 The student will recognize and demonstrate the meaning of equality in an equation. Related SOL: 4.4a Materials: Partners Explore Equality and Equations activity sheet (attached) Vocabulary equal symbol (=), equality, equation, expression, not equal symbol (≠), number sentence, open number sentence, reasoning Student/Teacher Actions: What should students be doing? What should teachers be doing? 1. Activate students’ prior knowledge about the equal sign. Formally assess what students already know and what misconceptions they have. Ask students to open their notebooks to a clean sheet of paper and draw lines to divide the sheet into thirds as shown below. Create a poster of the same graphic organizer for the room. This activity should take no more than 10 minutes; it is not a teaching opportunity but simply a data- collection activity. Equality and the Equal Sign (=) I know that- I wonder if- I learned that- 1 1 1 a. Let students know they will be creating a graphic organizer about equality and the equal sign; that is, what it means and how it is used. The organizer is to help them keep track of what they know, what they wonder, and what they learn. The “I know that” column is where they can write things they are pretty sure are correct. The “I wonder if” column is where they can write things they have questions about and also where they can put things they think may be true but they are not sure. -
Indexed Induction-Recursion
Indexed Induction-Recursion Peter Dybjer a;? aDepartment of Computer Science and Engineering, Chalmers University of Technology, 412 96 G¨oteborg, Sweden Email: [email protected], http://www.cs.chalmers.se/∼peterd/, Tel: +46 31 772 1035, Fax: +46 31 772 3663. Anton Setzer b;1 bDepartment of Computer Science, University of Wales Swansea, Singleton Park, Swansea SA2 8PP, UK, Email: [email protected], http://www.cs.swan.ac.uk/∼csetzer/, Tel: +44 1792 513368, Fax: +44 1792 295651. Abstract An indexed inductive definition (IID) is a simultaneous inductive definition of an indexed family of sets. An inductive-recursive definition (IRD) is a simultaneous inductive definition of a set and a recursive definition of a function from that set into another type. An indexed inductive-recursive definition (IIRD) is a combination of both. We present a closed theory which allows us to introduce all IIRDs in a natural way without much encoding. By specialising it we also get a closed theory of IID. Our theory of IIRDs includes essentially all definitions of sets which occur in Martin-L¨of type theory. We show in particular that Martin-L¨of's computability predicates for dependent types and Palmgren's higher order universes are special kinds of IIRD and thereby clarify why they are constructively acceptable notions. We give two axiomatisations. The first and more restricted one formalises a prin- ciple for introducing meaningful IIRD by using the data-construct in the original version of the proof assistant Agda for Martin-L¨of type theory. The second one admits a more general form of introduction rule, including the introduction rule for the intensional identity relation, which is not covered by the restricted one. -
Formal Systems: Combinatory Logic and -Calculus
INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS:COMBINATORY LOGIC AND λ-CALCULUS Andrew R. Plummer Department of Linguistics The Ohio State University 30 Sept., 2009 INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION OUTLINE 1 INTRODUCTION 2 APPLICATIVE SYSTEMS 3 USEFUL INFORMATION INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION COMBINATORY LOGIC We present the foundations of Combinatory Logic and the λ-calculus. We mean to precisely demonstrate their similarities and differences. CURRY AND FEYS (KOREAN FACE) The material discussed is drawn from: Combinatory Logic Vol. 1, (1958) Curry and Feys. Lambda-Calculus and Combinators, (2008) Hindley and Seldin. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS We begin with some definitions. FORMAL SYSTEMS A formal system is composed of: A set of terms; A set of statements about terms; A set of statements, which are true, called theorems. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS TERMS We are given a set of atomic terms, which are unanalyzed primitives. We are also given a set of operations, each of which is a mode for combining a finite sequence of terms to form a new term. Finally, we are given a set of term formation rules detailing how to use the operations to form terms. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS STATEMENTS We are given a set of predicates, each of which is a mode for forming a statement from a finite sequence of terms. We are given a set of statement formation rules detailing how to use the predicates to form statements. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS THEOREMS We are given a set of axioms, each of which is a statement that is unconditionally true (and thus a theorem). -
Wittgenstein, Turing and Gödel
Wittgenstein, Turing and Gödel Juliet Floyd Boston University Lichtenberg-Kolleg, Georg August Universität Göttingen Japan Philosophy of Science Association Meeting, Tokyo, Japan 12 June 2010 Wittgenstein on Turing (1946) RPP I 1096. Turing's 'Machines'. These machines are humans who calculate. And one might express what he says also in the form of games. And the interesting games would be such as brought one via certain rules to nonsensical instructions. I am thinking of games like the “racing game”. One has received the order "Go on in the same way" when this makes no sense, say because one has got into a circle. For that order makes sense only in certain positions. (Watson.) Talk Outline: Wittgenstein’s remarks on mathematics and logic Turing and Wittgenstein Gödel on Turing compared Wittgenstein on Mathematics and Logic . The most dismissed part of his writings {although not by Felix Mülhölzer – BGM III} . Accounting for Wittgenstein’s obsession with the intuitive (e.g. pictures, models, aspect perception) . No principled finitism in Wittgenstein . Detail the development of Wittgenstein’s remarks against background of the mathematics of his day Machine metaphors in Wittgenstein Proof in logic is a “mechanical” expedient Logical symbolisms/mathematical theories are “calculi” with “proof machinery” Proofs in mathematics (e.g. by induction) exhibit or show algorithms PR, PG, BB: “Can a machine think?” Language (thought) as a mechanism Pianola Reading Machines, the Machine as Symbolizing its own actions, “Is the human body a thinking machine?” is not an empirical question Turing Machines Turing resolved Hilbert’s Entscheidungsproblem (posed in 1928): Find a definite method by which every statement of mathematics expressed formally in an axiomatic system can be determined to be true or false based on the axioms. -
First-Order Logic
Chapter 5 First-Order Logic 5.1 INTRODUCTION In propositional logic, it is not possible to express assertions about elements of a structure. The weak expressive power of propositional logic accounts for its relative mathematical simplicity, but it is a very severe limitation, and it is desirable to have more expressive logics. First-order logic is a considerably richer logic than propositional logic, but yet enjoys many nice mathemati- cal properties. In particular, there are finitary proof systems complete with respect to the semantics. In first-order logic, assertions about elements of structures can be ex- pressed. Technically, this is achieved by allowing the propositional symbols to have arguments ranging over elements of structures. For convenience, we also allow symbols denoting functions and constants. Our study of first-order logic will parallel the study of propositional logic conducted in Chapter 3. First, the syntax of first-order logic will be defined. The syntax is given by an inductive definition. Next, the semantics of first- order logic will be given. For this, it will be necessary to define the notion of a structure, which is essentially the concept of an algebra defined in Section 2.4, and the notion of satisfaction. Given a structure M and a formula A, for any assignment s of values in M to the variables (in A), we shall define the satisfaction relation |=, so that M |= A[s] 146 5.2 FIRST-ORDER LANGUAGES 147 expresses the fact that the assignment s satisfies the formula A in M. The satisfaction relation |= is defined recursively on the set of formulae. -
Part 3: First-Order Logic with Equality
Part 3: First-Order Logic with Equality Equality is the most important relation in mathematics and functional programming. In principle, problems in first-order logic with equality can be handled by, e.g., resolution theorem provers. Equality is theoretically difficult: First-order functional programming is Turing-complete. But: resolution theorem provers cannot even solve problems that are intuitively easy. Consequence: to handle equality efficiently, knowledge must be integrated into the theorem prover. 1 3.1 Handling Equality Naively Proposition 3.1: Let F be a closed first-order formula with equality. Let ∼ 2/ Π be a new predicate symbol. The set Eq(Σ) contains the formulas 8x (x ∼ x) 8x, y (x ∼ y ! y ∼ x) 8x, y, z (x ∼ y ^ y ∼ z ! x ∼ z) 8~x,~y (x1 ∼ y1 ^ · · · ^ xn ∼ yn ! f (x1, : : : , xn) ∼ f (y1, : : : , yn)) 8~x,~y (x1 ∼ y1 ^ · · · ^ xn ∼ yn ^ p(x1, : : : , xn) ! p(y1, : : : , yn)) for every f /n 2 Ω and p/n 2 Π. Let F~ be the formula that one obtains from F if every occurrence of ≈ is replaced by ∼. Then F is satisfiable if and only if Eq(Σ) [ fF~g is satisfiable. 2 Handling Equality Naively By giving the equality axioms explicitly, first-order problems with equality can in principle be solved by a standard resolution or tableaux prover. But this is unfortunately not efficient (mainly due to the transitivity and congruence axioms). 3 Roadmap How to proceed: • Arbitrary binary relations. • Equations (unit clauses with equality): Term rewrite systems. Expressing semantic consequence syntactically. Entailment for equations. • Equational clauses: Entailment for clauses with equality. -
Logic, Sets, and Proofs David A
Logic, Sets, and Proofs David A. Cox and Catherine C. McGeoch Amherst College 1 Logic Logical Statements. A logical statement is a mathematical statement that is either true or false. Here we denote logical statements with capital letters A; B. Logical statements be combined to form new logical statements as follows: Name Notation Conjunction A and B Disjunction A or B Negation not A :A Implication A implies B if A, then B A ) B Equivalence A if and only if B A , B Here are some examples of conjunction, disjunction and negation: x > 1 and x < 3: This is true when x is in the open interval (1; 3). x > 1 or x < 3: This is true for all real numbers x. :(x > 1): This is the same as x ≤ 1. Here are two logical statements that are true: x > 4 ) x > 2. x2 = 1 , (x = 1 or x = −1). Note that \x = 1 or x = −1" is usually written x = ±1. Converses, Contrapositives, and Tautologies. We begin with converses and contrapositives: • The converse of \A implies B" is \B implies A". • The contrapositive of \A implies B" is \:B implies :A" Thus the statement \x > 4 ) x > 2" has: • Converse: x > 2 ) x > 4. • Contrapositive: x ≤ 2 ) x ≤ 4. 1 Some logical statements are guaranteed to always be true. These are tautologies. Here are two tautologies that involve converses and contrapositives: • (A if and only if B) , ((A implies B) and (B implies A)). In other words, A and B are equivalent exactly when both A ) B and its converse are true. -
Tool 1: Gender Terminology, Concepts and Definitions
TOOL 1 Gender terminology, concepts and def initions UNESCO Bangkok Office Asia and Pacific Regional Bureau for Education Mom Luang Pin Malakul Centenary Building 920 Sukhumvit Road, Prakanong, Klongtoei © Hadynyah/Getty Images Bangkok 10110, Thailand Email: [email protected] Website: https://bangkok.unesco.org Tel: +66-2-3910577 Fax: +66-2-3910866 Table of Contents Objectives .................................................................................................. 1 Key information: Setting the scene ............................................................. 1 Box 1: .......................................................................................................................2 Fundamental concepts: gender and sex ...................................................... 2 Box 2: .......................................................................................................................2 Self-study and/or group activity: Reflect on gender norms in your context........3 Self-study and/or group activity: Check understanding of definitions ................3 Gender and education ................................................................................. 4 Self-study and/or group activity: Gender and education definitions ...................4 Box 3: Gender parity index .....................................................................................7 Self-study and/or group activity: Reflect on girls’ and boys’ lives in your context .....................................................................................................................8 -
Comparison Identity Vs. Equality Identity Vs. Equality for Strings Notions of Equality Order
Comparison Comparisons and the Comparable • Something that we do a lot Interface • Can compare all kinds of data with respect to all kinds of comparison relations . Identity . Equality Lecture 14 . Order CS211 – Spring 2006 . Lots of others Identity vs. Equality Identity vs. Equality for Strings • For primitive types (e.g., int, long, float, double, boolean) . == and != are equality tests • Quiz: What are the results of the following tests? • For reference types (i.e., objects) . "hello".equals("hello") true . == and != are identity tests . In other words, they test if the references indicate the same address . "hello" == "hello" true in the Heap . "hello" == new String("hello") false • For equality of objects: use the equals( ) method . equals( ) is defined in class Object . Any class you create inherits equals from its parent class, but you can override it (and probably want to) Notions of equality Order • A is equal to B if A can be substituted for B anywhere • For numeric primitives • For all other reference . Identical things must be equal: == implies equals (e.g., int, float, long, types double) • Immutable values are equal if they represent same value! . <, >, <=, >= do not work . Use <, >, <=, >= • Not clear you want them . (new Integer(2)).equals(new Integer(2)) to work: suppose we . == is not an abstract operation compare People • For reference types that Compare by name? • Mutable values can be distinguished by assignment. correspond to primitive Compare by height? class Foo { int f; Foo(int g) { f = g; } } weight? types Compare by SSN? Foo x = new Foo(2); . As of Java 5.0, Java does CUID? Foo y = new Foo(2); Autoboxing and Auto- . -
Dismantling Racism in Mathematics Instruction Exercises for Educators to Reflect on Their Own Biases to Transform Their Instructional Practice
A Pathway to MAY 2021 Equitable Math Instruction Dismantling Racism in Mathematics Instruction Exercises for educators to reflect on their own biases to transform their instructional practice STRIDE 1 Dismantling Racism STRIDE 1 in Mathematics Instruction This tool provides teachers an opportunity to examine THEMES their actions, beliefs, and values around teaching math- Teacher Beliefs ematics. The framework for deconstructing racism in GUIDING PRINCIPLES mathematics offers essential characteristics of antiracist Culturally relevant curricula and math educators and critical approaches to dismantling practices designed to increase access for students of color. white supremacy in math classrooms by making visi- Promoting antiracist ble the toxic characteristics of white supremacy culture mathematics instruction. (Jones and Okun 2001; dismantling Racism 2016) with respect to math. Building on the framework, teachers engage with critical praxis in order to shift their instruc- CONTENT DEVELOPERS tional beliefs and practices towards antiracist math ed- Sonia Michelle Cintron ucation. By centering antiracism, we model how to be Math Content Specialist UnboundEd antiracist math educators with accountability. Dani Wadlington Director of Mathematics Education Quetzal Education Consulting Andre ChenFeng HOW TO USE THIS TOOL Ph.D. Student Education at Claremont Graduate While primarily for math educa- • Teachers should use this work- University tors, this text advocates for a book to self-reflect on individual collective approach to dismantling -
Quantum Set Theory Extending the Standard Probabilistic Interpretation of Quantum Theory (Extended Abstract)
Quantum Set Theory Extending the Standard Probabilistic Interpretation of Quantum Theory (Extended Abstract) Masanao Ozawa∗ Graduate School of Information Science, Nagoya University Nagoya, Japan [email protected] The notion of equality between two observables will play many important roles in foundations of quantum theory. However, the standard probabilistic interpretation based on the conventional Born formula does not give the probability of equality relation for a pair of arbitrary observables, since the Born formula gives the probability distribution only for a commuting family of observables. In this paper, quantum set theory developed by Takeuti and the present author is used to systematically extend the probabilistic interpretation of quantum theory to define the probability of equality relation for a pair of arbitrary observables. Applications of this new interpretation to measurement theory are discussed briefly. 1 Introduction Set theory provides foundations of mathematics. All the mathematical notions like numbers, functions, relations, and structures are defined in the axiomatic set theory, ZFC (Zermelo-Fraenkel set theory with the axiom of choice), and all the mathematical theorems are required to be provable in ZFC [15]. Quan- tum set theory instituted by Takeuti [14] and developed by the present author [11] naturally extends the logical basis of set theory from classical logic to quantum logic [1]. Accordingly, quantum set theory extends quantum logical approach to quantum foundations from propositional logic to predicate logic and set theory. Hence, we can expect that quantum set theory will provide much more systematic inter- pretation of quantum theory than the conventional quantum logic approach [3]. The notion of equality between quantum observables will play many important roles in foundations of quantum theory, in particular, in the theory of measurement and disturbance [9, 10]. -
Warren Goldfarb, Notes on Metamathematics
Notes on Metamathematics Warren Goldfarb W.B. Pearson Professor of Modern Mathematics and Mathematical Logic Department of Philosophy Harvard University DRAFT: January 1, 2018 In Memory of Burton Dreben (1927{1999), whose spirited teaching on G¨odeliantopics provided the original inspiration for these Notes. Contents 1 Axiomatics 1 1.1 Formal languages . 1 1.2 Axioms and rules of inference . 5 1.3 Natural numbers: the successor function . 9 1.4 General notions . 13 1.5 Peano Arithmetic. 15 1.6 Basic laws of arithmetic . 18 2 G¨odel'sProof 23 2.1 G¨odelnumbering . 23 2.2 Primitive recursive functions and relations . 25 2.3 Arithmetization of syntax . 30 2.4 Numeralwise representability . 35 2.5 Proof of incompleteness . 37 2.6 `I am not derivable' . 40 3 Formalized Metamathematics 43 3.1 The Fixed Point Lemma . 43 3.2 G¨odel'sSecond Incompleteness Theorem . 47 3.3 The First Incompleteness Theorem Sharpened . 52 3.4 L¨ob'sTheorem . 55 4 Formalizing Primitive Recursion 59 4.1 ∆0,Σ1, and Π1 formulas . 59 4.2 Σ1-completeness and Σ1-soundness . 61 4.3 Proof of Representability . 63 3 5 Formalized Semantics 69 5.1 Tarski's Theorem . 69 5.2 Defining truth for LPA .......................... 72 5.3 Uses of the truth-definition . 74 5.4 Second-order Arithmetic . 76 5.5 Partial truth predicates . 79 5.6 Truth for other languages . 81 6 Computability 85 6.1 Computability . 85 6.2 Recursive and partial recursive functions . 87 6.3 The Normal Form Theorem and the Halting Problem . 91 6.4 Turing Machines .